{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.autograd import Variable\n",
    "import torch.nn.functional as F\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/Documents/mtcars.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
       "0          Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n",
       "1      Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n",
       "2         Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
       "3     Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n",
       "4  Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n",
       "\n",
       "   carb  \n",
       "0     4  \n",
       "1     4  \n",
       "2     1  \n",
       "3     1  \n",
       "4     2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x252fa643b50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(1234)    # reproducible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = torch.unsqueeze(torch.from_numpy(np.array(df.qsec)),dim=1)\n",
    "y = torch.unsqueeze(torch.from_numpy(np.array(df.mpg)),dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[16.4600],\n",
       "        [17.0200],\n",
       "        [18.6100],\n",
       "        [19.4400],\n",
       "        [17.0200],\n",
       "        [20.2200],\n",
       "        [15.8400],\n",
       "        [20.0000],\n",
       "        [22.9000],\n",
       "        [18.3000]], dtype=torch.float64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[21.0000],\n",
       "        [21.0000],\n",
       "        [22.8000],\n",
       "        [21.4000],\n",
       "        [18.7000],\n",
       "        [18.1000],\n",
       "        [14.3000],\n",
       "        [24.4000],\n",
       "        [22.8000],\n",
       "        [19.2000]], dtype=torch.float64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Net(torch.nn.Module):\n",
    "    def __init__(self, n_feature, n_hidden, n_output):\n",
    "        super(Net, self).__init__()\n",
    "        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer\n",
    "        self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.hidden(x))      # activation function for hidden layer\n",
    "        x = self.predict(x)             # linear output\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (hidden): Linear(in_features=1, out_features=20, bias=True)\n",
      "  (predict): Linear(in_features=20, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "net = Net(n_feature=1, n_hidden=20, n_output=1)   \n",
    "net.double()\n",
    "print(net)  # Neural network architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = torch.optim.SGD(net.parameters(), lr=0.2)\n",
    "loss_func = torch.nn.MSELoss()  \n",
    "# this is for regression mean squared loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGD (\n",
       "Parameter Group 0\n",
       "    dampening: 0\n",
       "    lr: 0.2\n",
       "    momentum: 0\n",
       "    nesterov: False\n",
       "    weight_decay: 0\n",
       ")"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSELoss()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Turn the interactive mode on\n",
    "plt.ion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LAABk73ZJF6e03xwRS5Pb96tx4rT5MM1zrN2/3sMf0IfgUHvssphLxnw1YPYISwAAZCwi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25282497240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/fiILh1bG5jOwdcISAABDN+4zM5p1TCZ7lgAAGLqzzcyMA806JpOwBADA0I37zIxmHZPJ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\n6CAsAQAAdBCWAAAAOvx/hzNmKryVlBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2528258e6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(100):\n",
    "    prediction = net(x)     # input x and predict based on x\n",
    "    loss = loss_func(prediction, y)     # must be (1. nn output, 2. target)\n",
    "    optimizer.zero_grad()   # clear gradients for next train\n",
    "    loss.backward()         # backpropagation, compute gradients\n",
    "    optimizer.step()        # apply gradients\n",
    "\n",
    "    if t % 50 == 0:\n",
    "        # plot and show learning process\n",
    "        plt.cla()\n",
    "        plt.scatter(x.data.numpy(), y.data.numpy())\n",
    "        plt.plot(x.data.numpy(), prediction.data.numpy(), 'g-', lw=3)\n",
    "        plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy())\n",
    "        plt.show()\n",
    "plt.ioff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = torch.optim.SGD(net.parameters(), lr=0.001)\n",
    "loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AOvx/hzNmKryVlBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x252825ce668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AOvx/hzNmKryVlBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x252824cc5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AOvx/hzNmKryVlBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x252839e6cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AOvx/hzNmKryVlBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25283b61898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AOvx/hzNmKryVlBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25282701780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AOvx/hzNmKryVlBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2528390bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AOvx/hzNmKryVlBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25283ac5198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AOvx/hzNmKryVlBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25283a30390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AOvx/hzNmKryVlBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25283df13c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AOvx/hzNmKryVlBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25283ba81d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(1000):\n",
    "    prediction = net(x)     # input x and predict based on x\n",
    "    loss = loss_func(prediction, y)     # must be (1. nn output, 2. target)\n",
    "    optimizer.zero_grad()   # clear gradients for next train\n",
    "    loss.backward()         # backpropagation, compute gradients\n",
    "    optimizer.step()        # apply gradients\n",
    "\n",
    "    if t % 100 == 0:\n",
    "        # plot and show learning process\n",
    "        plt.cla()\n",
    "        plt.scatter(x.data.numpy(), y.data.numpy())\n",
    "        plt.plot(x.data.numpy(), prediction.data.numpy(), 'g-', lw=3)\n",
    "        plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy())\n",
    "        plt.show()\n",
    "plt.ioff() #Turn the interactive mode off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
       "0          Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n",
       "1      Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n",
       "2         Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
       "3     Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n",
       "4  Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n",
       "\n",
       "   carb  \n",
       "0     4  \n",
       "1     4  \n",
       "2     1  \n",
       "3     1  \n",
       "4     2  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = torch.unsqueeze(torch.from_numpy(np.array(df.mpg)),dim=1)\n",
    "y = torch.unsqueeze(torch.from_numpy(np.array(df.qsec)),dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = torch.optim.SGD(net.parameters(), lr=0.2)\n",
    "loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.ion() #Turn the interactive mode on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R4ytnvLXbyslTue3I/UkiqMCmmeZlqR2+68Ez3iM1SU499ngO3/XgjEYE7Eb2TFlqJ06e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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x252824777b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x252838c3c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25282578518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25282701f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x252827e4d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(1000):\n",
    "    prediction = net(x)     # input x and predict based on x\n",
    "    loss = loss_func(prediction, y)     # must be (1. nn output, 2. target)\n",
    "    optimizer.zero_grad()   # clear gradients for next train\n",
    "    loss.backward()         # backpropagation, compute gradients\n",
    "    optimizer.step()        # apply gradients\n",
    "\n",
    "    if t % 200 == 0:\n",
    "        # plot and show learning process\n",
    "        plt.cla()\n",
    "        plt.scatter(x.data.numpy(), y.data.numpy())\n",
    "        plt.plot(x.data.numpy(), prediction.data.numpy(), 'g-', lw=3)\n",
    "        plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy())\n",
    "        plt.show()\n",
    "plt.ioff() #Turn the interactive mode off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
       "0          Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n",
       "1      Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n",
       "2         Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
       "3     Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n",
       "4  Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n",
       "\n",
       "   carb  \n",
       "0     4  \n",
       "1     4  \n",
       "2     1  \n",
       "3     1  \n",
       "4     2  "
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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